






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__		:							List[str] 				=				"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=8       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase__      :  str				      =			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						):
          super().__init__()

          self.register_modules(
              unet=_lowerCAmelCase	,						scheduler=_lowerCAmelCase	,						movq=_lowerCAmelCase	,						)
          UpperCAmelCase__      :  Optional[Any]				      =			2 ** (len(self.movq.config.block_out_channels		) - 1)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          if latents is None:
               UpperCAmelCase__      :  Any				      =			randn_tensor(_lowerCAmelCase	,						generator=_lowerCAmelCase	,						device=_lowerCAmelCase	,						dtype=_lowerCAmelCase		)
          else:
               if latents.shape != shape:
                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
               UpperCAmelCase__      :  str				      =			latents.to(_lowerCAmelCase		)

          UpperCAmelCase__      :  Dict				      =			latents * scheduler.init_noise_sigma
          return latents




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0		):
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

          UpperCAmelCase__      :  Any				      =			torch.device(f"cuda:{gpu_id}"		)

          UpperCAmelCase__      :  int				      =			[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(_lowerCAmelCase	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0		):
          if is_accelerate_available() and is_accelerate_version(""">="""	,						"""0.17.0.dev0"""		):
               from accelerate import cpu_offload_with_hook
          else:
               raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""		)

          UpperCAmelCase__      :  str				      =			torch.device(f"cuda:{gpu_id}"		)

          if self.device.type != "cpu":
               self.to("""cpu"""	,						silence_dtype_warnings=_lowerCAmelCase		)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          UpperCAmelCase__      :  Any				      =			None
          for cpu_offloaded_model in [self.unet, self.movq]:
               UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			cpu_offload_with_hook(_lowerCAmelCase	,						_lowerCAmelCase	,						prev_module_hook=_lowerCAmelCase		)

          # We'll offload the last model manually.
          UpperCAmelCase__      :  str				      =			hook




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def   __UpperCAmelCase  (				self		):
          if not hasattr(self.unet	,						"""_hf_hook"""		):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(_lowerCAmelCase	,						"""_hf_hook"""		)
                   and hasattr(module._hf_hook	,						"""execution_device"""		)
                   and module._hf_hook.execution_device is not None
               ):
                    return torch.device(module._hf_hook.execution_device		)
          return self.device




     @torch.no_grad()
     @replace_example_docstring(_lowerCAmelCase		)
     def __call__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = 512	,						_lowerCAmelCase = 512	,						_lowerCAmelCase = 100	,						_lowerCAmelCase = 4.0	,						_lowerCAmelCase = 1	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = "pil"	,						_lowerCAmelCase = True	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			self._execution_device

          UpperCAmelCase__      :  Dict				      =			guidance_scale > 1.0

          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  Union[str, Any]				      =			torch.cat(_lowerCAmelCase	,						dim=0		)
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  List[str]				      =			torch.cat(_lowerCAmelCase	,						dim=0		)
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  List[str]				      =			torch.cat(_lowerCAmelCase	,						dim=0		)

          UpperCAmelCase__      :  Optional[int]				      =			image_embeds.shape[0] * num_images_per_prompt

          if do_classifier_free_guidance:
               UpperCAmelCase__      :  str				      =			image_embeds.repeat_interleave(_lowerCAmelCase	,						dim=0		)
               UpperCAmelCase__      :  Tuple				      =			negative_image_embeds.repeat_interleave(_lowerCAmelCase	,						dim=0		)
               UpperCAmelCase__      :  Dict				      =			hint.repeat_interleave(_lowerCAmelCase	,						dim=0		)

          UpperCAmelCase__      :  Tuple				      =			torch.cat([negative_image_embeds, image_embeds]	,						dim=0		).to(dtype=self.unet.dtype	,						device=_lowerCAmelCase		)
          UpperCAmelCase__      :  Any				      =			torch.cat([hint, hint]	,						dim=0		).to(dtype=self.unet.dtype	,						device=_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase	,						device=_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			self.scheduler.timesteps

          UpperCAmelCase__      :  List[Any]				      =			self.movq.config.latent_channels

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[str]				      =			downscale_height_and_width(_lowerCAmelCase	,						_lowerCAmelCase	,						self.movq_scale_factor		)

          # create initial latent
          UpperCAmelCase__      :  Tuple				      =			self.prepare_latents(
              (batch_size, num_channels_latents, height, width)	,						image_embeds.dtype	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						self.scheduler	,						)

          for i, t in enumerate(self.progress_bar(_lowerCAmelCase		)		):
               # expand the latents if we are doing classifier free guidance
               UpperCAmelCase__      :  int				      =			torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

               UpperCAmelCase__      :  int				      =			{"""image_embeds""": image_embeds, """hint""": hint}
               UpperCAmelCase__      :  str				      =			self.unet(
                   sample=_lowerCAmelCase	,						timestep=_lowerCAmelCase	,						encoder_hidden_states=_lowerCAmelCase	,						added_cond_kwargs=_lowerCAmelCase	,						return_dict=_lowerCAmelCase	,						)[0]

               if do_classifier_free_guidance:
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			noise_pred.split(latents.shape[1]	,						dim=1		)
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			noise_pred.chunk(2		)
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			variance_pred.chunk(2		)
                    UpperCAmelCase__      :  Optional[Any]				      =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    UpperCAmelCase__      :  int				      =			torch.cat([noise_pred, variance_pred_text]	,						dim=1		)

               if not (
                   hasattr(self.scheduler.config	,						"""variance_type"""		)
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[int]				      =			noise_pred.split(latents.shape[1]	,						dim=1		)

               # compute the previous noisy sample x_t -> x_t-1
               UpperCAmelCase__      :  Any				      =			self.scheduler.step(
                   _lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						generator=_lowerCAmelCase	,						)[0]
          # post-processing
          UpperCAmelCase__      :  Dict				      =			self.movq.decode(_lowerCAmelCase	,						force_not_quantize=_lowerCAmelCase		)["""sample"""]

          if output_type not in ["pt", "np", "pil"]:
               raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"		)

          if output_type in ["np", "pil"]:
               UpperCAmelCase__      :  int				      =			image * 0.5 + 0.5
               UpperCAmelCase__      :  List[str]				      =			image.clamp(0	,						1		)
               UpperCAmelCase__      :  Tuple				      =			image.cpu().permute(0	,						2	,						3	,						1		).float().numpy()

          if output_type == "pil":
               UpperCAmelCase__      :  Tuple				      =			self.numpy_to_pil(_lowerCAmelCase		)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_lowerCAmelCase		)







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A						=			logging.get_logger(__name__)


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    Any:
		# initialize config
		if "resnet-50" in model_name:
				_lowerCamelCase				   =  ResNetConfig.from_pretrained('microsoft/resnet-50'							)
		elif "resnet-101" in model_name:
				_lowerCamelCase				   =  ResNetConfig.from_pretrained('microsoft/resnet-101'							)
		else:
				raise ValueError('Model name should include either resnet50 or resnet101'							)

		_lowerCamelCase				   =  DetrConfig(use_timm_backbone=UpperCamelCase			,       backbone_config=UpperCamelCase							)

		# set label attributes
		_lowerCamelCase				   =  'panoptic' in model_name
		if is_panoptic:
				_lowerCamelCase				   =  2_50
		else:
				_lowerCamelCase				   =  91
				_lowerCamelCase				   =  'huggingface/label-files'
				_lowerCamelCase				   =  'coco-detection-id2label.json'
				_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
				_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
				_lowerCamelCase				   =  idalabel
				_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		return config, is_panoptic


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]							)   ->    Dict:
		# here we list all keys to be renamed (original name on the left, our name on the right)
		_lowerCamelCase				   =  []

		# stem
		# fmt: off
		rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')							)
		rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')							)
		rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')							)
		rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')							)
		rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')							)
		# stages
		for stage_idx in range(len(config.backbone_config.depths							)							):
				for layer_idx in range(config.backbone_config.depths[stage_idx]							):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
								    )							)
						# 3 convs
						for i in range(3							):
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
								    )							)
    # fmt: on

		for i in range(config.encoder_layers							):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (
				        F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
				        F"""encoder.layers.{i}.self_attn.out_proj.weight""",
				    )							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")							)
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
				        F"""decoder.layers.{i}.self_attn.out_proj.weight""",
				    )							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")							)
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
				        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
				    )							)
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
				        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
				    )							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")							)

		# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
		rename_keys.extend(
		    [
		        ('input_proj.weight', 'input_projection.weight'),
		        ('input_proj.bias', 'input_projection.bias'),
		        ('query_embed.weight', 'query_position_embeddings.weight'),
		        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
		        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
		        ('class_embed.weight', 'class_labels_classifier.weight'),
		        ('class_embed.bias', 'class_labels_classifier.bias'),
		        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
		        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
		        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
		        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
		        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
		        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
		    ]							)

		return rename_keys


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Any							)   ->    Any:
		_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
		_lowerCamelCase				   =  val


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				List[Any]=False							)   ->    List[str]:
		_lowerCamelCase				   =  ''
		if is_panoptic:
				_lowerCamelCase				   =  'detr.'

		# first: transformer encoder
		for i in range(6							):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""							)
				# next, add query, keys and values (in that order) to the state dict
				_lowerCamelCase				   =  in_proj_weight[:2_56, :]
				_lowerCamelCase				   =  in_proj_bias[:2_56]
				_lowerCamelCase				   =  in_proj_weight[2_56:5_12, :]
				_lowerCamelCase				   =  in_proj_bias[2_56:5_12]
				_lowerCamelCase				   =  in_proj_weight[-2_56:, :]
				_lowerCamelCase				   =  in_proj_bias[-2_56:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6							):
				# read in weights + bias of input projection layer of self-attention
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""							)
				# next, add query, keys and values (in that order) to the state dict
				_lowerCamelCase				   =  in_proj_weight[:2_56, :]
				_lowerCamelCase				   =  in_proj_bias[:2_56]
				_lowerCamelCase				   =  in_proj_weight[2_56:5_12, :]
				_lowerCamelCase				   =  in_proj_bias[2_56:5_12]
				_lowerCamelCase				   =  in_proj_weight[-2_56:, :]
				_lowerCamelCase				   =  in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				_lowerCamelCase				   =  state_dict.pop(
				    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""							)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				_lowerCamelCase				   =  in_proj_weight_cross_attn[:2_56, :]
				_lowerCamelCase				   =  in_proj_bias_cross_attn[:2_56]
				_lowerCamelCase				   =  in_proj_weight_cross_attn[2_56:5_12, :]
				_lowerCamelCase				   =  in_proj_bias_cross_attn[2_56:5_12]
				_lowerCamelCase				   =  in_proj_weight_cross_attn[-2_56:, :]
				_lowerCamelCase				   =  in_proj_bias_cross_attn[-2_56:]


def 	lowerCamelCase					(	)   ->    Tuple:
		_lowerCamelCase				   =  'http://images.cocodataset.org/val2017/000000039769.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							)

		return im


@torch.no_grad()
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				Dict=None			,       UpperCamelCase     :				str=False							)   ->    Dict:
		_lowerCamelCase   ,							_lowerCamelCase				   =  get_detr_config(UpperCamelCase							)

		# load original model from torch hub
		_lowerCamelCase				   =  {
		    'detr-resnet-50': 'detr_resnet50',
		    'detr-resnet-101': 'detr_resnet101',
		}
		logger.info(F"""Converting model {model_name}..."""							)
		_lowerCamelCase				   =  torch.hub.load('facebookresearch/detr'			,       model_name_to_original_name[model_name]			,       pretrained=UpperCamelCase							).eval()
		_lowerCamelCase				   =  detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(UpperCamelCase							):
				if is_panoptic:
						_lowerCamelCase				   =  'detr.' + src
				rename_key(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
		# query, key and value matrices need special treatment
		read_in_q_k_v(UpperCamelCase			,       is_panoptic=UpperCamelCase							)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		_lowerCamelCase				   =  'detr.model.' if is_panoptic else 'model.'
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith('detr'							)
						    and not key.startswith('class_labels_classifier'							)
						    and not key.startswith('bbox_predictor'							)
						):
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val
						elif key.startswith('bbox_attention'							) or key.startswith('mask_head'							):
								continue
						else:
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val
				else:
						if not key.startswith('class_labels_classifier'							) and not key.startswith('bbox_predictor'							):
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val

    # finally, create HuggingFace model and load state dict
		_lowerCamelCase				   =  DetrForSegmentation(UpperCamelCase							) if is_panoptic else DetrForObjectDetection(UpperCamelCase							)
		model.load_state_dict(UpperCamelCase							)
		model.eval()

		# verify our conversion on an image
		_lowerCamelCase				   =  'coco_panoptic' if is_panoptic else 'coco_detection'
		_lowerCamelCase				   =  DetrImageProcessor(format=UpperCamelCase							)

		_lowerCamelCase				   =  processor(images=prepare_img()			,       return_tensors='pt'							)
		_lowerCamelCase				   =  encoding['pixel_values']

		_lowerCamelCase				   =  detr(UpperCamelCase							)
		_lowerCamelCase				   =  model(UpperCamelCase							)

		assert torch.allclose(outputs.logits			,       original_outputs['pred_logits']			,       atol=1e-3							)
		assert torch.allclose(outputs.pred_boxes			,       original_outputs['pred_boxes']			,       atol=1e-3							)
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks			,       original_outputs['pred_masks']			,       atol=1e-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				# Save model and image processor
				logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
				Path(UpperCamelCase							).mkdir(exist_ok=UpperCamelCase							)
				model.save_pretrained(UpperCamelCase							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				# Upload model and image processor to the hub
				logger.info('Uploading PyTorch model and image processor to the hub...'							)
				model.push_to_hub(F"""nielsr/{model_name}"""							)
				processor.push_to_hub(F"""nielsr/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='detr-resnet-50',
				    type=str,
				    choices=['detr-resnet-50', 'detr-resnet-101'],
				    help='Name of the DETR model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
				A						=			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)